
import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
   pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''align_text_model'''



     def __init__(				self							:							Optional[int]     ,						_A							:							Union[str, Any]=3_0522     ,						_A							:							List[str]=768     ,						_A							:							List[Any]=12     ,						_A							:							int=12     ,						_A							:							str=3072     ,						_A							:							Optional[Any]="gelu"     ,						_A							:							Union[str, Any]=0.1     ,						_A							:							List[str]=0.1     ,						_A							:							List[str]=512     ,						_A							:							Union[str, Any]=2     ,						_A							:							Optional[int]=0.02     ,						_A							:							Optional[int]=1e-12     ,						_A							:							List[str]=0     ,						_A							:							Optional[Any]="absolute"     ,						_A							:							Tuple=True     ,						**_A							:							str     ,						):







       """simple docstring"""




       super().__init__(**_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		vocab_size
       __SCREAMING_SNAKE_CASE      :	Dict					=		hidden_size
       __SCREAMING_SNAKE_CASE      :	str					=		num_hidden_layers
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		num_attention_heads
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	Any					=		intermediate_size
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		hidden_dropout_prob
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE      :	int					=		max_position_embeddings
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		type_vocab_size
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		layer_norm_eps
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		position_embedding_type
       __SCREAMING_SNAKE_CASE      :	Tuple					=		use_cache
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		pad_token_id




     @classmethod
     def  UpperCAmelCase__							(				cls							:							int     ,						_A							:							Union[str, os.PathLike]     ,						**_A							:							Union[str, Any] ):







       """simple docstring"""




       cls._set_token_in_kwargs(_A )

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		cls.get_config_dict(_A     ,						**_A )

       # get the text config dict if we are loading from AlignConfig
       if config_dict.get('''model_type''' ) == "align":
         __SCREAMING_SNAKE_CASE      :	str					=		config_dict['''text_config''']

       if "model_type" in config_dict and hasattr(cls     ,						'''model_type''' ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
             F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

       return cls.from_dict(_A     ,						**_A )






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''align_vision_model'''



     def __init__(				self							:							Dict     ,						_A							:							int = 3     ,						_A							:							int = 600     ,						_A							:							float = 2.0     ,						_A							:							float = 3.1     ,						_A							:							int = 8     ,						_A							:							List[int] = [3, 3, 5, 3, 5, 5, 3]     ,						_A							:							List[int] = [32, 16, 24, 40, 80, 112, 192]     ,						_A							:							List[int] = [16, 24, 40, 80, 112, 192, 320]     ,						_A							:							List[int] = []     ,						_A							:							List[int] = [1, 2, 2, 2, 1, 2, 1]     ,						_A							:							List[int] = [1, 2, 2, 3, 3, 4, 1]     ,						_A							:							List[int] = [1, 6, 6, 6, 6, 6, 6]     ,						_A							:							float = 0.25     ,						_A							:							str = "swish"     ,						_A							:							int = 2560     ,						_A							:							str = "mean"     ,						_A							:							float = 0.02     ,						_A							:							float = 0.0_01     ,						_A							:							float = 0.99     ,						_A							:							float = 0.2     ,						**_A							:							Optional[Any]     ,						):







       """simple docstring"""




       super().__init__(**_A )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		num_channels
       __SCREAMING_SNAKE_CASE      :	Tuple					=		image_size
       __SCREAMING_SNAKE_CASE      :	Any					=		width_coefficient
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		depth_coefficient
       __SCREAMING_SNAKE_CASE      :	Tuple					=		depth_divisor
       __SCREAMING_SNAKE_CASE      :	Any					=		kernel_sizes
       __SCREAMING_SNAKE_CASE      :	Tuple					=		in_channels
       __SCREAMING_SNAKE_CASE      :	str					=		out_channels
       __SCREAMING_SNAKE_CASE      :	str					=		depthwise_padding
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		strides
       __SCREAMING_SNAKE_CASE      :	List[str]					=		num_block_repeats
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		expand_ratios
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		squeeze_expansion_ratio
       __SCREAMING_SNAKE_CASE      :	int					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	str					=		hidden_dim
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		pooling_type
       __SCREAMING_SNAKE_CASE      :	Any					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	int					=		batch_norm_eps
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		batch_norm_momentum
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		drop_connect_rate
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		sum(_A ) * 4




     @classmethod
     def  UpperCAmelCase__							(				cls							:							Any     ,						_A							:							Union[str, os.PathLike]     ,						**_A							:							Optional[Any] ):







       """simple docstring"""




       cls._set_token_in_kwargs(_A )

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		cls.get_config_dict(_A     ,						**_A )

       # get the vision config dict if we are loading from AlignConfig
       if config_dict.get('''model_type''' ) == "align":
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		config_dict['''vision_config''']

       if "model_type" in config_dict and hasattr(cls     ,						'''model_type''' ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
             F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

       return cls.from_dict(_A     ,						**_A )






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''align'''
     lowerCAmelCase_							   =			True



     def __init__(				self							:							str     ,						_A							:							Optional[int]=None     ,						_A							:							Optional[int]=None     ,						_A							:							Dict=640     ,						_A							:							int=1.0     ,						_A							:							Any=0.02     ,						**_A							:							int     ,						):







       """simple docstring"""




       super().__init__(**_A )

       if text_config is None:
         __SCREAMING_SNAKE_CASE      :	Dict					=		{}
         logger.info('''text_config is None. Initializing the AlignTextConfig with default values.''' )

       if vision_config is None:
         __SCREAMING_SNAKE_CASE      :	Any					=		{}
         logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.''' )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		AlignTextConfig(**_A )
       __SCREAMING_SNAKE_CASE      :	int					=		AlignVisionConfig(**_A )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		projection_dim
       __SCREAMING_SNAKE_CASE      :	str					=		temperature_init_value
       __SCREAMING_SNAKE_CASE      :	Any					=		initializer_range



     @classmethod
     def  UpperCAmelCase__							(				cls							:							Dict     ,						_A							:							AlignTextConfig     ,						_A							:							AlignVisionConfig     ,						**_A							:							str ):







       """simple docstring"""




       return cls(text_config=text_config.to_dict()     ,						vision_config=vision_config.to_dict()     ,						**_A )




     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		copy.deepcopy(self.__dict__ )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.text_config.to_dict()
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.vision_config.to_dict()
       __SCREAMING_SNAKE_CASE      :	int					=		self.__class__.model_type
       return output



import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


